Passed

gpkit.tests.from_paths.TestFiles.test_gpkitmodels_SP_SimPleAC_SimPleAC_py_cvxopt (from gpkit.tests.from_paths.TestFiles-20210712155344)

Standard Output

Using solver 'cvxopt'
 for 11 free variables
  in 14 posynomial inequalities.
Solving took 0.169 seconds.
Using solver 'cvxopt'
 for 9 free variables
  in 15 posynomial inequalities.
Solving took 0.0413 seconds.
Using solver 'cvxopt'
 for 25 free variables
  in 35 posynomial inequalities.
Solving took 0.0872 seconds.
Using solver 'cvxopt'
 for 25 free variables
  in 35 posynomial inequalities.
Solving took 0.0392 seconds.
Starting a sequence of GP solves
 for 53 free variables
  in 11 locally-G
...[truncated 4865 chars]...
 seconds.
Solved cost was 5717.

GP Solve 2
Using solver 'cvxopt'
 for 21 free variables
  in 22 posynomial inequalities.
Solving took 0.0169 seconds.
Solved cost was 4538.

GP Solve 3
Using solver 'cvxopt'
 for 21 free variables
  in 22 posynomial inequalities.
Solving took 0.022 seconds.
Solved cost was 4536.

GP Solve 4
Using solver 'cvxopt'
 for 21 free variables
  in 22 posynomial inequalities.
Solving took 0.0149 seconds.
Solved cost was 4536.

Solving took 0.135 seconds and 4 GP solves.
	

Standard Error

/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/cvxopt/venv2_gpkit/lib/python3.7/site-packages/numpy/core/shape_base.py:65: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
  ary = asanyarray(ary)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/cvxopt/venv2_gpkit/lib/python3.7/site-packages/numpy/core/shape_base.py:65: UnitStrippedWarning: The unit of the quantity is stripped when downcasti
...[truncated 2103 chars]...
to ndarray.
  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
/Users/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/macys_VM/optimizer/cvxopt/gpkit/constraints/sgp.py:179: UserWarning: SGP not convergent: Cost rose by 6.1% (1588.62 to 1685.18) on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note convergence is not guaranteed for models with SignomialEqualities.
  % (100*(cost - prevcost)/prevcost, prevcost, cost, len(self.gps)))